Distributed AI intelligent remote inspection method and system based on edge calculation

Abstract

The invention relates to the technical field of intelligent remote inspection, and discloses a distributed AI intelligent remote inspection method and system based on edge computing, and the method comprises the steps: obtaining an inspection region set, carrying out the region sorting of the inspection region set, obtaining an ordered inspection region set, sequentially extracting an ordered inspection region from the ordered inspection region set, and storing the ordered inspection region in a database; performing the following operations on the extracted ordered inspection areas: obtaining electromagnetic interference intensity of the ordered inspection areas, setting an optimal inspection path, confirming the state of a lighting module, performing line inspection, obtaining a line image set, performing insulation skin damage point identification on a corrected image, obtaining a damaged area set, and obtaining a voltage intensity set of the damaged area set; and maintaining the damaged area to obtain a maintenance area, constructing a maintenance database based on a maintenance area set, and completing distributed AI intelligent remote inspection based on edge calculation based on the maintenance database. According to the invention, the efficiency and reliability of remote inspection can be improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent remote inspection technology, and in particular to a distributed AI intelligent remote inspection method, system, electronic device and computer-readable storage medium based on edge computing. Background Art

[0002] Edge computing is a technology that processes, stores and analyzes data as close to the data source as possible. Distributed AI is a technology that distributes the computing tasks of AI models to multiple computing nodes. Intelligent remote inspection is a method of using artificial intelligence technology to automatically inspect and monitor remote devices or systems.

[0003] Traditional remote inspection methods have problems such as insufficient real-time performance, high bandwidth pressure, high maintenance costs, and lack of intelligence, making it difficult to meet the needs of complex scenarios. Therefore, how to improve the efficiency and reliability of remote inspection is a technical problem that needs to be solved urgently. Summary of the invention

[0004] The present invention provides a distributed AI intelligent remote inspection method based on edge computing and a computer-readable storage medium, the main purpose of which is to improve the intelligence level of high-temperature testing of titanium rods and reduce excessive consumption of time and human resources.

[0005] To achieve the above objectives, the present invention provides a distributed AI intelligent remote inspection method based on edge computing, comprising: Obtain an inspection area set, and use a preset inspection order to sort the inspection area set to obtain an ordered inspection area set, wherein an ordered inspection area includes a substation, and the substation includes: a plurality of transmission lines; An ordered inspection area is extracted from the ordered inspection area set in sequence, and the following operations are performed on the extracted ordered inspection areas: Obtain the electromagnetic interference intensity of the orderly inspection area and set the optimal inspection path according to the electromagnetic interference intensity; Collecting the ambient brightness, confirming the state of the lighting module according to the ambient brightness, and obtaining the state of the lighting module, wherein the lighting module includes: a minus-one counter, a zero detector, and a cycle counter; receiving an intelligent remote inspection instruction, and performing line inspection on a plurality of power transmission lines in the orderly inspection area according to the intelligent remote inspection instruction, the state of the lighting module and the optimal inspection path, to obtain a line image set, wherein one line image includes one or more power transmission lines; Each line image in the line image set is subjected to distortion correction to obtain a correction image set, and one correction image is extracted from the correction image set in turn, and the following operations are performed on each of the extracted correction images: Identify insulation damage points on the corrected image to obtain a damage area set, and obtain a voltage intensity set of the damage area set, wherein the damage area set includes one or more damage areas; If it is confirmed that there is a voltage intensity greater than a preset voltage intensity threshold in the voltage intensity concentration, a pre-constructed alarm signal is sent to a pre-constructed maintenance unit. After the maintenance unit receives the alarm signal, the maintenance unit is used to repair the damaged area to obtain a maintenance area, and the maintenance areas are summarized to obtain a maintenance area group. Summarizing the maintenance area groups corresponding to the orderly inspection areas to obtain a maintenance area group set; Build a maintenance database based on maintenance area groups, and complete distributed AI intelligent remote inspection based on edge computing based on the maintenance database.

[0006] Optionally, obtaining the electromagnetic interference intensity of the orderly inspection area includes: Obtain regional environmental parameters and regional current intensity of the orderly inspection area, and obtain regional air density values ​​based on the regional environmental parameters, wherein the regional environmental parameters include: regional temperature and regional air pressure; Determine whether the regional air density value is within the preset theoretical density range; If it is confirmed that the regional air density value is within the theoretical density interval, the regional air density value is corrected using the regional current intensity to obtain a corrected density air value; The modified density air value is used to calculate the electromagnetic interference intensity of multiple power transmission lines in the orderly inspection area, wherein the calculation formula of the electromagnetic interference intensity is as follows: , , in, Indicates the corrected density air value, Indicates the preset standard temperature. represents the regional temperature, represents the regional air pressure, Indicates the preset standard air pressure. represents the regional current intensity, Indicates the electromagnetic interference intensity. Indicates the preset insulation roughness coefficient. Indicates the preset transmission line radius.

[0007] Optionally, setting the optimal inspection path according to the electromagnetic interference intensity includes: The electromagnetic intensity attenuation value is obtained according to the electromagnetic interference intensity, and the inspection field length and inspection field width of the pre-built UAV are determined based on the electromagnetic intensity attenuation value, wherein the UAV includes: a camera, and the inspection field length and inspection field width are expressed as: , , , in, Indicates the length of the inspection field of view. Indicates the inspection field width. represents the proportional adjustment coefficient, Indicates the length of the camera, Indicates the width of the camera, Indicates the preset flight altitude. Indicates the relative height difference of the camera. Represents the electromagnetic intensity attenuation value, Indicates the electromagnetic interference demarcation point, Indicates the line measurement distance; The optimal inspection path in the ordered inspection area is calculated using the pre-built optimal path formula, inspection field length, and inspection field width.

[0008] Optionally, the optimal path formula is as follows: , in, represents the optimal inspection path, Indicates the total inspection length of the drone. represents the line spacing of the transmission line, Represents the number of transmission lines.

[0009] Optionally, determining the state of the lighting module according to the ambient brightness to obtain the state of the lighting module includes: Get the ambient brightness value according to the ambient brightness; If the ambient brightness value is within the preset standard inspection brightness range, a start signal is obtained, the lighting module is turned on based on the start signal, and after confirming that the turned-on lighting module is the preset turn-on lighting module, the all-zero detector is confirmed as the initial state indicator; If the ambient brightness value is not within the preset standard inspection brightness range, a shutdown signal is obtained, the lighting module is turned off based on the shutdown signal, and after confirming that the turned-off lighting module is the preset turned-off lighting module, the all-zero detector is confirmed as the initial state indicator; The initial value of the decrement counter is set based on the initial state indicator and the cycle counter to obtain the initial value N of the decrement counter, and a decrement operation is performed on the initial value N of the decrement counter using a pre-constructed frequency-divided clock signal to obtain a decrement output value; If the decrement output value is not zero, the decrement output value is used as the initial value N of the decrement counter, and the process returns to the step of performing a decrement operation on the initial value N of the decrement counter using the pre-built divided clock signal until the decrement output value is zero, then the initial state indicator is confirmed to be a target all-zero detector, and the state of the lighting module is confirmed based on the target all-zero detector.

[0010] Optionally, performing distortion correction on each line image in the line image set to obtain a corrected image set includes: Extract one line image from the line image set in turn, and perform the following operations on each of the extracted line images: Construct a plane rectangular coordinate system according to the line image, obtain the coordinates of each pixel point in the line image according to the plane rectangular coordinate system, and obtain a pixel point coordinate set; Extract one pixel point coordinate from the pixel point coordinate set one by one, and perform the following operations on the extracted pixel point coordinates: The pixel coordinates are distorted and corrected to obtain the corrected pixel coordinates, where the corrected pixel coordinates are expressed as: , in, Indicates the coordinates of the corrected pixel points. Represents the pixel coordinates, ( ) represents the coordinate deviation value, represents the radial distortion parameter, represents the tangential distortion parameter, Indicates the horizontal coordinate of the corrected pixel coordinates, Indicates the ordinate of the corrected pixel coordinates, Represents the horizontal coordinate of the pixel point, Indicates the vertical coordinate of the pixel; Summarizing the coordinates of the corrected pixel points to obtain a corrected pixel point coordinate set, and acquiring a corrected image based on the corrected pixel point coordinate set; The corrected images are aggregated to obtain a corrected image set.

[0011] Optionally, the step of identifying insulation damage points on the corrected image to obtain a damaged area set includes: Identifying a target power transmission line from the corrected image, wherein the corrected image includes a plurality of target power transmission lines; Perform the following operations on the target transmission lines: Dividing the target transmission line by using a preset division range to obtain multiple line segments; Extract one route segment from multiple route segments one by one, and perform the following operations on each of the extracted route segments: Obtain a standard grayscale interval, filter the line segment to obtain a filtered line segment, perform grayscale conversion on the filtered line segment to obtain a grayscale line segment, wherein the grayscale line segment includes: a plurality of grayscale pixels, and the grayscale pixel includes: a grayscale pixel value, wherein the grayscale pixel value is the grayscale value of the grayscale pixel; The following operations are performed on each of the multiple grayscale pixels: Determine whether the grayscale pixel value corresponding to the grayscale pixel is within the standard grayscale interval; If the grayscale pixel value corresponding to the grayscale pixel is not in the standard grayscale interval, the grayscale pixel is recorded as an abnormal pixel; Summarize the abnormal pixels to obtain multiple abnormal pixels, confirm the number of abnormal pixels among the multiple abnormal pixels, and obtain the number of abnormal pixels; Compare the number of abnormal pixels with the preset standard number of pixels; If the number of abnormal pixels is greater than the preset number of standard pixels, a unit damaged area composed of multiple abnormal pixels is obtained; If the number of abnormal pixels is not greater than the preset number of standard pixels, returning to the step of extracting one line segment from the multiple line segments in sequence until all the multiple line segments are extracted; The unit damaged areas are summarized to obtain a unit damaged area group corresponding to the target transmission line, and the unit damaged area groups are summarized to obtain a damaged area set corresponding to the corrected image.

[0012] Optionally, obtaining the voltage intensity set of the damaged area set includes: The damaged areas are extracted from the damaged area set in sequence, and the following operations are performed on the extracted damaged areas: De-noise the damaged area to obtain a denoised area image; Extract edge points from the denoised area image to obtain an edge point set, calculate the leakage voltage component of each edge point in the edge point set, and sum the leakage voltage components of each edge point in the edge point set to obtain a total leakage voltage component; The voltage intensity of the damaged area is calculated based on the total leakage voltage component, where the voltage intensity is calculated as follows: , in, Indicates the voltage intensity, represents pi, represents the capacitance parameter of the transmission line, Indicates the edge point concentration The leakage voltage component of each edge point is Indicates the number of edge points in the edge point set; The voltage intensities are summed up to obtain a voltage intensity set.

[0013] Optionally, denoising the damaged area to obtain a denoised area image includes: The pre-built Gaussian kernel formula is used to perform convolution operation on each damaged image pixel in the damaged area image to obtain a denoised pixel point set, where the Gaussian kernel formula is as follows: , in, represents the denoised pixel, represents the standard deviation of the Gaussian function, represents the exponential function with base e as base, Represents the horizontal coordinate of the denoised pixel, Represents the ordinate of the denoised pixel point, Indicates the center position of the Gaussian kernel; A denoised area image is confirmed according to the denoised pixel point set.

[0014] To achieve the above objectives, the present invention also provides a distributed AI intelligent remote inspection system based on edge computing, comprising: The inspection area management module is used to obtain an inspection area set, and use a preset inspection order to sort the inspection area set to obtain an ordered inspection area set, wherein an ordered inspection area includes a substation, and the substation includes: multiple transmission lines; The inspection path planning module is used to extract an orderly inspection area from the orderly inspection area set in turn, and perform the following operations on the extracted orderly inspection areas: obtain the electromagnetic interference intensity of the orderly inspection area, set the optimal inspection path according to the electromagnetic interference intensity, collect the ambient brightness, confirm the state of the lighting module according to the ambient brightness, and obtain the state of the lighting module, wherein the lighting module includes: a minus-one counter, a zero detector and a cycle counter, receive intelligent remote inspection instructions, and perform line inspection on multiple power transmission lines in the orderly inspection area according to the intelligent remote inspection instructions, the state of the lighting module and the optimal inspection path to obtain a line image set, wherein one line image includes one or more power transmission lines; The line image processing module is used to perform distortion correction on each line image in the line image set to obtain a correction image set, extract a correction image from the correction image set in turn, and perform the following operations on the extracted correction images: identify insulation damage points on the correction image to obtain a damage area set, and obtain a voltage intensity set of the damage area set, wherein the damage area set includes one or more damage areas; The line maintenance management module is used to send a pre-built alarm signal to a pre-built maintenance unit if it is confirmed that there is a voltage intensity greater than a preset voltage intensity threshold in the voltage intensity concentration. When the maintenance unit receives the alarm signal, it uses the maintenance unit to repair the damaged area to obtain the maintenance area, and summarizes the maintenance area to obtain a maintenance area group, summarizes the maintenance area groups corresponding to the ordered inspection areas to obtain a maintenance area group set, builds a maintenance database based on the maintenance area group set, and completes distributed AI intelligent remote inspection based on edge computing based on the maintenance database.

[0015] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising: A memory storing at least one instruction; The processor executes the instructions stored in the memory to implement the above-mentioned distributed AI intelligent remote inspection method based on edge computing.

[0016] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned distributed AI intelligent remote inspection method based on edge computing.

[0017] The present invention is to solve the problem described in the background technology. The present invention obtains an inspection area set, and uses a preset inspection sequence to sort the inspection area set to obtain an ordered inspection area set, wherein an ordered inspection area includes a substation, and the substation includes: multiple transmission lines. The present invention can optimize the inspection path, reduce the inspection time and improve the inspection efficiency through the preset inspection sequence, and extract an ordered inspection area from the ordered inspection area set in turn, and perform the following operations on the extracted ordered inspection areas: The present invention processes the inspection areas one by one to realize modular management, which is convenient for system maintenance and expansion, obtains the electromagnetic interference strength of the ordered inspection area, and sets the optimal inspection path according to the electromagnetic interference strength. The present invention detects the electromagnetic interference strength and sets the optimal inspection path according to the electromagnetic interference strength. The magnetic interference intensity setting can avoid areas with serious interference, select a path with better signal, ensure the stability and accuracy of data transmission, collect ambient brightness, confirm the state of the lighting module according to the ambient brightness, and obtain the state of the lighting module, wherein the lighting module includes: a minus-one counter, a zero detector, and a cycle counter. The present invention automatically adjusts the lighting module according to the ambient brightness to ensure that images can be clearly captured even in insufficient light conditions, thereby improving image quality, receiving intelligent remote inspection instructions, and performing line inspections on multiple power transmission lines in the orderly inspection area according to the intelligent remote inspection instructions, the state of the lighting module, and the optimal inspection path to obtain a line image set, wherein a line image includes one or more power transmission lines, and the present invention combines inspection instructions, illumination module status, and optimal inspection paths to obtain a line image set. The invention can identify the state and the optimal path of the transmission line, efficiently collect the image data of the transmission line, ensure that each transmission line can be inspected, improve the integrity of the data, perform distortion correction on each line image in the line image set, obtain a correction image set, extract one correction image from the correction image set in turn, and perform the following operations on the extracted correction images: the present invention can correct the geometric distortion of the image through distortion correction, improve the clarity and accuracy of the image, and the corrected image is more convenient for subsequent analysis and processing. The insulation damage point of the correction image is identified to obtain a damage area set, and the voltage strength set of the damage area set is obtained, wherein the damage area set includes one or more damage areas. The present invention can highlight the insulation damage point by identifying the insulation damage point. Abnormal areas such as skin damage are easy to identify. If it is confirmed that the voltage intensity is concentrated with a voltage intensity greater than a preset voltage intensity threshold, a pre-constructed alarm signal is sent to a pre-constructed maintenance unit. After the maintenance unit receives the alarm signal, the damaged area is repaired by the maintenance unit to obtain a maintenance area, and the maintenance area is summarized to obtain a maintenance area group. When a high-voltage area is detected, the present invention immediately issues an alarm to remind relevant personnel to take emergency measures, summarizes the maintenance area group corresponding to the orderly inspection area, and obtains a maintenance area group set. The present invention centrally processes maintenance tasks, optimizes the allocation and utilization of maintenance resources, constructs a maintenance database based on the maintenance area group set, and completes distributed AI intelligent remote inspection based on edge computing based on the maintenance database.The maintenance database can record historical maintenance data, provide reference for future inspections and maintenance, and realize data-driven decision-making. Therefore, the present invention can improve the efficiency and reliability of remote inspections. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of a distributed AI intelligent remote inspection method based on edge computing provided in one embodiment of the present invention; Figure 2 A functional module diagram of a distributed AI intelligent remote inspection system based on edge computing provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of an electronic device for implementing the distributed AI intelligent remote inspection method based on edge computing provided in one embodiment of the present invention.

[0019] Description of reference numerals: 1. Electronic device; 10. Processor; 11. Memory; 12. Bus.

[0020] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0021] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0022] The embodiment of the present application provides a distributed AI intelligent remote inspection method based on edge computing. The execution subject of the distributed AI intelligent remote inspection method based on edge computing includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the distributed AI intelligent remote inspection method based on edge computing can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0023] Reference Figure 1 As shown, it is a flow chart of a distributed AI intelligent remote inspection method based on edge computing provided by an embodiment of the present invention. In this embodiment, the distributed AI intelligent remote inspection method based on edge computing includes: S1. Obtain an inspection area set, and sort the inspection area set by area using a preset inspection sequence to obtain an ordered inspection area set.

[0024] Specifically, an orderly inspection area includes a substation, and the substation includes: multiple transmission lines.

[0025] It should be explained that the inspection area set refers to the set of all areas that need to be inspected. The inspection order refers to a pre-set inspection order. Optionally, the inspection areas in the inspection area set are sorted from large to small according to the probability of failure of the substations corresponding to the inspection areas in history to obtain the inspection order. Area sorting refers to the operation of sorting each inspection area in the inspection area set. The ordered inspection area set refers to the set of inspection areas arranged according to the preset inspection order.

[0026] S2. Extract one ordered inspection area from the ordered inspection area set in turn, and perform the following operations on each of the extracted ordered inspection areas: obtain the electromagnetic interference intensity of the ordered inspection area, and set an optimal inspection path according to the electromagnetic interference intensity.

[0027] In detail, the obtaining of the electromagnetic interference intensity of the orderly inspection area includes: Obtain regional environmental parameters and regional current intensity of the orderly inspection area, and obtain regional air density values ​​based on the regional environmental parameters, wherein the regional environmental parameters include: regional temperature and regional air pressure; Determine whether the regional air density value is within the preset theoretical density range; If it is confirmed that the regional air density value is within the theoretical density interval, the regional air density value is corrected using the regional current intensity to obtain a corrected density air value; The modified density air value is used to calculate the electromagnetic interference intensity of multiple power transmission lines in the orderly inspection area, wherein the calculation formula of the electromagnetic interference intensity is as follows: , , in, Indicates the corrected density air value, Indicates the preset standard temperature. represents the regional temperature, represents the regional air pressure, Indicates the preset standard air pressure. represents the regional current intensity, Indicates the electromagnetic interference intensity. Indicates the preset insulation roughness coefficient. Indicates the preset transmission line radius.

[0028] It is understandable that the theoretical density range is a pre-set standard range. If the regional air density value is not within the theoretical density range, the calculation result of the electromagnetic interference intensity will be inaccurate. If the regional air density value is within the theoretical density range, the calculation result of the electromagnetic interference intensity is more accurate. Using the regional current intensity to correct the regional air density value can further improve the accuracy of the electromagnetic interference intensity calculation result.

[0029] It should be explained that the acquisition of regional environmental parameters of the orderly inspection area refers to the detection of regional environmental parameters of the orderly inspection area by using temperature sensors, air pressure sensors and current intensity sensors. Regional temperature refers to the ambient temperature in the orderly inspection area. Regional air pressure and regional current intensity refer to the ambient air pressure in the orderly inspection area and the current intensity in multiple power transmission lines, respectively. In the step of acquiring the regional air density value based on the regional environmental parameters, the following formula is used to obtain the regional air density value: , in, Indicates the regional air density value, represents the gas constant.

[0030] It should be explained that the modified density air value refers to the regional air density value within the theoretical density range, which is used to more accurately reflect the actual environmental conditions. Standard temperature refers to the temperature under standard atmospheric conditions. Standard air pressure refers to the air pressure under standard atmospheric conditions. The insulation roughness coefficient refers to the roughness coefficient of the insulation surface of the transmission line, which is predetermined according to the processing technology. The transmission line radius is the radius predetermined according to the transmission line design.

[0031] In detail, setting the optimal inspection path according to the electromagnetic interference intensity includes: The electromagnetic intensity attenuation value is obtained according to the electromagnetic interference intensity, and the inspection field length and inspection field width of the pre-built UAV are determined based on the electromagnetic intensity attenuation value, wherein the UAV includes: a camera, and the inspection field length and inspection field width are expressed as: , , , in, Indicates the length of the inspection field of view. Indicates the inspection field width. represents the proportional adjustment coefficient, Indicates the length of the camera, Indicates the width of the camera, Indicates the preset flight altitude. Indicates the relative height difference of the camera. Represents the electromagnetic intensity attenuation value, Indicates the electromagnetic interference demarcation point, Indicates the line measurement distance; The optimal inspection path in the ordered inspection area is calculated using the pre-built optimal path formula, inspection field length, and inspection field width.

[0032] It should be explained that the inspection field of view length refers to the length along the transmission line that the camera can cover during the inspection process of the drone. The inspection field of view width refers to the width in the vertical direction of the transmission line that the camera can cover during the inspection process of the drone. The proportional adjustment coefficient is an artificially set coefficient used to correct the length and width of the camera field of view. The flight altitude refers to a pre-set height of the drone relative to the ground during the inspection process. The relative position height difference refers to the height of the camera installation position relative to the bottom of the drone. The electromagnetic interference demarcation point refers to a boundary value, which is the electromagnetic interference safety demarcation point that the drone can withstand during flight.

[0033] Importantly, the step of obtaining the electromagnetic intensity attenuation value according to the electromagnetic interference intensity is: calculating the electromagnetic intensity attenuation value using the following formula: , in, represents a natural constant, Indicates the preset attenuation coefficient.

[0034] It can be understood that the attenuation coefficient refers to a coefficient used to describe the attenuation degree of electromagnetic interference intensity with distance. The larger the attenuation coefficient, the faster the electromagnetic interference intensity decays with increasing distance. The smaller the attenuation coefficient, the slower the electromagnetic interference intensity decays with increasing distance.

[0035] In detail, the optimal path formula is as follows: , in, represents the optimal inspection path, Indicates the total inspection length of the drone. represents the line spacing of the transmission line, Represents the number of transmission lines.

[0036] It should be explained that the optimal inspection path refers to the most efficient drone flight path calculated by the optimal path formula based on factors such as electromagnetic interference intensity, drone inspection field of view, and transmission line layout, aiming to improve inspection efficiency and safety.

[0037] S3. Collect the ambient brightness, confirm the state of the lighting module according to the ambient brightness, and obtain the state of the lighting module.

[0038] Specifically, the lighting module includes: a minus-one counter, an all-zero detector and a cycle counter.

[0039] In detail, the step of determining the state of the lighting module according to the ambient brightness to obtain the state of the lighting module includes: Get the ambient brightness value according to the ambient brightness; If the ambient brightness value is within the preset standard inspection brightness range, a start signal is obtained, the lighting module is turned on based on the start signal, and after confirming that the turned-on lighting module is the preset turn-on lighting module, the all-zero detector is confirmed as the initial state indicator; If the ambient brightness value is not within the preset standard inspection brightness range, a shutdown signal is obtained, the lighting module is turned off based on the shutdown signal, and after confirming that the turned-off lighting module is the preset turned-off lighting module, the all-zero detector is confirmed as the initial state indicator; The initial value of the decrement counter is set based on the initial state indicator and the cycle counter to obtain the initial value N of the decrement counter, and a decrement operation is performed on the initial value N of the decrement counter using a pre-constructed frequency-divided clock signal to obtain a decrement output value; If the decrement output value is not zero, the decrement output value is used as the initial value N of the decrement counter, and the process returns to the step of performing a decrement operation on the initial value N of the decrement counter using the pre-built divided clock signal until the decrement output value is zero, then the initial state indicator is confirmed to be a target all-zero detector, and the state of the lighting module is confirmed based on the target all-zero detector.

[0040] It should be explained that the collected ambient brightness refers to the use of a photoresistor to collect the ambient brightness. The ambient brightness value refers to the specific value obtained by measuring the ambient brightness using a photoresistor. The standard inspection brightness interval refers to a preset range of brightness values. When the ambient brightness value is within the standard inspection brightness interval, it indicates that the brightness in the environment is appropriate and no additional lighting is required. An all-zero detector refers to a logic device that indicates that the lighting module should be turned on when the ambient brightness is within the standard inspection brightness interval, and that the lighting module should be turned off when the ambient brightness is not within the standard inspection brightness interval. An initial state indicator refers to a device that confirms the initial state of the lighting module based on the signal of the all-zero detector. A decrement operation refers to the use of a decrement operator to perform an operation of subtracting 1 from the initial value N of a minus-one counter. Turning on a lighting module refers to a lighting module whose light is in the on state. Turning off a lighting module refers to a lighting module whose light is in the off state.

[0041] Importantly, a cycle counter refers to a device for measuring the time from when the state of the lighting module changes to the end of a preset time period. A decrement counter refers to a counter that performs a decrement operation each time. A divided clock signal refers to a clock signal that is used to control the decrement frequency of the decrement counter. A decrement output value refers to the value output by the decrement counter after each decrement operation. A target zero detector refers to a device that confirms that the initial state indicator is a target zero detector when the decrement output value of the decrement counter is zero, and is used to confirm the final state of the lighting module. A turn-on signal refers to a signal used to indicate that the system should turn on the lighting module. A turn-off signal refers to a signal used to indicate that the system should turn off the lighting module.

[0042] S4. Receive intelligent remote inspection instructions, and perform line inspection on multiple power transmission lines in the orderly inspection area according to the intelligent remote inspection instructions, the state of the lighting module and the optimal inspection path to obtain a line image set.

[0043] Specifically, a line image includes one or more transmission lines.

[0044] It should be explained that the intelligent remote inspection command is a command issued by humans to guide the drone to inspect a specific area. The lighting module status refers to the on / off status of the lighting equipment during the inspection process. The transmission line refers to the line in the power transmission network, which is used to transmit electric energy over long distances. For example, high-voltage transmission lines and low-voltage distribution lines. The line image set refers to the image set of multiple transmission lines collected by the camera during the inspection process. Line inspection refers to the operation of shooting multiple transmission lines along the optimal inspection path using a preset inspection frequency.

[0045] S5. Perform distortion correction on each line image in the line image set to obtain a corrected image set.

[0046] In detail, the distortion correction is performed on each line image in the line image set to obtain a corrected image set, including: Extract one line image from the line image set in turn, and perform the following operations on each of the extracted line images: Construct a plane rectangular coordinate system according to the line image, obtain the coordinates of each pixel point in the line image according to the plane rectangular coordinate system, and obtain a pixel point coordinate set; Extract one pixel point coordinate from the pixel point coordinate set one by one, and perform the following operations on the extracted pixel point coordinates: The pixel coordinates are distorted and corrected to obtain the corrected pixel coordinates, where the corrected pixel coordinates are expressed as: , in, Indicates the coordinates of the corrected pixel points. Represents the pixel coordinates, ( ) represents the coordinate deviation value, represents the radial distortion parameter, represents the tangential distortion parameter, Indicates the horizontal coordinate of the corrected pixel coordinates, Indicates the ordinate of the corrected pixel coordinates, Represents the horizontal coordinate of the pixel point, Indicates the vertical coordinate of the pixel; Summarizing the coordinates of the corrected pixel points to obtain a corrected pixel point coordinate set, and acquiring a corrected image based on the corrected pixel point coordinate set; The corrected images are aggregated to obtain a corrected image set.

[0047] It should be explained that the step of constructing a plane rectangular coordinate system based on the line image is: construct a plane rectangular coordinate system with the lower left corner of the line image as the origin. The pixel point coordinate set refers to the set of coordinates of all pixel points in the line image on the plane rectangular coordinate system. Distortion correction refers to the operation of correcting pixel point coordinates using radial distortion parameters and tangential distortion parameters. Corrected pixel point coordinates refer to the coordinates obtained after the distortion correction operation, which is used to be affected by the camera lens. The original image may be distorted, causing the straight lines in the line image to look curved or distorted in shape, so pixel distortion correction is required.

[0048] It should be explained that the correction pixel point coordinate set refers to the set consisting of all correction pixel point coordinates. The radial distortion parameter refers to the parameter used to correct the phenomenon that the pixels at the edge of the line image are offset inward or outward. The tangential distortion parameter refers to the parameter used to correct the pixel position offset in the line image caused by the tangential distortion of the lens. The correction image refers to the image composed of the correction pixel point coordinate set. The correction image set refers to the set consisting of all correction images after distortion correction.

[0049] S6. Extract one corrected image from the corrected image set in turn, and perform the following operations on the extracted corrected images: identify insulation damage points on the corrected images to obtain a damaged area set, and obtain a voltage intensity set of the damaged area set, wherein the damaged area set includes one or more damaged areas.

[0050] In detail, the insulation damage points of the correction image are identified to obtain a damaged area set, including: Identifying a target power transmission line from the corrected image, wherein the corrected image includes a plurality of target power transmission lines; Perform the following operations on the target transmission lines: Dividing the target transmission line by using a preset division range to obtain multiple line segments; Extract one route segment from multiple route segments one by one, and perform the following operations on each of the extracted route segments: Obtain a standard grayscale interval, filter the line segment to obtain a filtered line segment, perform grayscale conversion on the filtered line segment to obtain a grayscale line segment, wherein the grayscale line segment includes: a plurality of grayscale pixels, and the grayscale pixel includes: a grayscale pixel value, wherein the grayscale pixel value is the grayscale value of the grayscale pixel; The following operations are performed on each of the multiple grayscale pixels: Determine whether the grayscale pixel value corresponding to the grayscale pixel is within the standard grayscale interval; If the grayscale pixel value corresponding to the grayscale pixel is not in the standard grayscale interval, the grayscale pixel is recorded as an abnormal pixel; Summarize the abnormal pixels to obtain multiple abnormal pixels, confirm the number of abnormal pixels among the multiple abnormal pixels, and obtain the number of abnormal pixels; Compare the number of abnormal pixels with the preset standard number of pixels; If the number of abnormal pixels is greater than the preset number of standard pixels, a unit damaged area composed of multiple abnormal pixels is obtained; If the number of abnormal pixels is not greater than the preset number of standard pixels, returning to the step of extracting one line segment from the multiple line segments in sequence until all the multiple line segments are extracted; The unit damaged areas are summarized to obtain a unit damaged area group corresponding to the target transmission line, and the unit damaged area groups are summarized to obtain a damaged area set corresponding to the corrected image.

[0051] It should be explained that the target transmission line refers to an image of a transmission line to be detected extracted from the rectified image. The method for identifying the target transmission line from the rectified image described in the embodiment of the present invention is a prior art, such as an edge detection algorithm, which will not be described in detail here.

[0052] Importantly, the division range refers to a pre-set range that divides the target transmission line into multiple small segments. For example, the division range is 3 cm. The line segment refers to the image of a section of wire obtained by dividing the target transmission line using the division range. The filtering refers to the operation of filtering the line segment using a Gaussian filter. The filtered line segment refers to the image corresponding to the filtered line segment. The technology for graying the filtered line segment described in the embodiment of the present invention is a prior art and will not be repeated here. The grayscale line segment refers to the image corresponding to the grayscale filtered line segment. The method for obtaining the standard grayscale interval is: the detection personnel shoot the target transmission line without any damage under different weather conditions from the historical monitoring time period to obtain a set of captured images, and perform the same image processing on the captured image set as the method for obtaining the corresponding image of the grayscale line segment in the embodiment of the present invention, so as to obtain a continuous interval composed of the grayscale values ​​of the undamaged transmission line. The continuous interval is the standard grayscale interval.

[0053] It should be explained that the number of abnormal pixels refers to the number of pixels whose grayscale values ​​are not within the standard grayscale range. The number of standard pixels refers to a pre-set number used to determine whether the abnormal pixels are noise points in the line segment image. Grayscale pixels refer to pixels in the grayscale line segment. Unit damaged area refers to the image corresponding to the area of ​​the largest circumscribed rectangle formed by multiple abnormal pixels. Unit damaged area group refers to the set of all unit damaged areas. Damaged area set refers to the set of all damaged area groups in the corrected image.

[0054] Exemplarily, there are three transmission lines in the corrected image, the first line segment in the first transmission line has one unit damaged area, the second line segment in the second transmission line has two unit damaged areas, and the third transmission line has no unit damaged area. Then, the one unit damaged area of ​​the first transmission line is the unit damaged area group corresponding to the first transmission line, and the two unit damaged areas of the second transmission line are the unit damaged area group corresponding to the second transmission line. The unit damaged area group of the first transmission line and the unit damaged area group of the second transmission line are summarized to obtain a damaged area set.

[0055] In detail, the step of obtaining the voltage intensity set of the damaged area set includes: The damaged areas are extracted from the damaged area set in sequence, and the following operations are performed on the extracted damaged areas: De-noise the damaged area to obtain a denoised area image; Extract edge points from the denoised area image to obtain an edge point set, calculate the leakage voltage component of each edge point in the edge point set, and sum the leakage voltage components of each edge point in the edge point set to obtain a total leakage voltage component; The voltage intensity of the damaged area is calculated based on the total leakage voltage component, where the voltage intensity is calculated as follows: , in, Indicates the voltage intensity, represents pi, represents the capacitance parameter of the transmission line, Indicates the edge point concentration The leakage voltage component of each edge point is Indicates the number of edge points in the edge point set; The voltage intensities are summed up to obtain a voltage intensity set.

[0056] It should be explained that the denoised area image refers to an image obtained by denoising the damaged area. The extraction of edge points from the denoised area image refers to extracting edge points of the transmission line from the denoised area image using an edge detection algorithm. The edge detection algorithm described in the embodiment of the present invention is a prior art and will not be described in detail here.

[0057] It should be explained that the edge point set refers to the set of all extracted edge points. The step of calculating the leakage voltage component of each edge point in the edge point set is: extracting edge points from the denoised area image, finding the corresponding position in the actual transmission line according to the edge point, and detecting the leakage voltage component of the corresponding position. The total leakage voltage component refers to the sum of the leakage voltage components of all edge points in the edge point set. The capacitance parameter refers to the parameter of the capacitance characteristic of the transmission line.

[0058] In detail, the denoising of the damaged area to obtain the denoised area image includes: The pre-built Gaussian kernel formula is used to perform convolution operation on each damaged image pixel in the damaged area image to obtain a denoised pixel point set, where the Gaussian kernel formula is as follows: , in, represents the denoised pixel, represents the standard deviation of the Gaussian function, represents the exponential function with base e as base, Represents the horizontal coordinate of the denoised pixel, Represents the ordinate of the denoised pixel point, Indicates the center position of the Gaussian kernel; A denoised area image is confirmed according to the denoised pixel point set.

[0059] It should be explained that the convolution operation refers to the operation of using the Gaussian kernel formula as a sliding window to cover each damaged image pixel in the image, and for each damaged image pixel, calculating the sum of the products of the pixel values ​​in the neighborhood of the damaged image pixel and the Gaussian kernel. The standard deviation of the Gaussian function refers to a pre-set value. The larger the standard deviation of the Gaussian function, the stronger the denoising effect, and the smaller the standard deviation of the Gaussian function, the weaker the denoising effect. The center position of the Gaussian kernel refers to the center point of the Gaussian kernel matrix, which is usually located at the geometric center of the matrix.

[0060] S7. If it is confirmed that there is a voltage intensity greater than a preset voltage intensity threshold in the voltage intensity concentration, a pre-constructed alarm signal is sent to a pre-constructed maintenance unit. When the maintenance unit receives the alarm signal, it uses the maintenance unit to repair the damaged area to obtain a maintenance area, and summarizes the maintenance areas to obtain a maintenance area group.

[0061] It should be explained that the voltage intensity threshold refers to a pre-set voltage value. The maintenance area obtained by using the maintenance unit to repair the damaged area refers to replacing the power line or repairing the insulation skin of the damaged area according to the alarm signal. The maintenance area refers to the damaged area after the maintenance process. The maintenance area group refers to the collection of all maintenance areas. The maintenance unit is a unit that can repair the damaged area.

[0062] S8. Summarize the maintenance area groups corresponding to the orderly inspection areas to obtain a maintenance area group set.

[0063] It should be explained that the maintenance area group set refers to a set formed by aggregating the maintenance area groups corresponding to all the ordered inspection areas.

[0064] S9. Build a maintenance database based on the maintenance area group set, and complete distributed AI intelligent remote inspection based on edge computing based on the maintenance database.

[0065] It should be explained that the steps of constructing a maintenance database based on a maintenance area set are: obtaining maintenance data from the maintenance area, wherein the maintenance data includes: maintenance records, historical fault data, maintenance time and maintenance measures, storing maintenance area groups corresponding to different orderly inspection areas in different tables, obtaining maintenance table groups, and using the maintenance table groups as a maintenance database. The main function of constructing a maintenance database based on a maintenance area group set is to provide a centralized platform to store and manage maintenance data, facilitate access and maintenance, and assist in formulating maintenance strategies and predictive maintenance plans based on historical data and analysis results.

[0066] The present invention is to solve the problem described in the background technology. The present invention obtains an inspection area set, and uses a preset inspection sequence to sort the inspection area set to obtain an ordered inspection area set, wherein an ordered inspection area includes a substation, and the substation includes: multiple transmission lines. The present invention can optimize the inspection path, reduce the inspection time and improve the inspection efficiency through the preset inspection sequence, and extract an ordered inspection area from the ordered inspection area set in turn, and perform the following operations on the extracted ordered inspection areas: The present invention processes the inspection areas one by one to realize modular management, which is convenient for system maintenance and expansion, obtains the electromagnetic interference strength of the ordered inspection area, and sets the optimal inspection path according to the electromagnetic interference strength. The present invention detects the electromagnetic interference strength and sets the optimal inspection path according to the electromagnetic interference strength. The magnetic interference intensity setting can avoid areas with serious interference, select a path with better signal, ensure the stability and accuracy of data transmission, collect ambient brightness, confirm the state of the lighting module according to the ambient brightness, and obtain the state of the lighting module, wherein the lighting module includes: a minus-one counter, a zero detector, and a cycle counter. The present invention automatically adjusts the lighting module according to the ambient brightness to ensure that images can be clearly captured even in insufficient light conditions, thereby improving image quality, receiving intelligent remote inspection instructions, and performing line inspections on multiple power transmission lines in the orderly inspection area according to the intelligent remote inspection instructions, the state of the lighting module, and the optimal inspection path to obtain a line image set, wherein a line image includes one or more power transmission lines, and the present invention combines inspection instructions, illumination module status, and optimal inspection paths to obtain a line image set. The invention can identify the state and the optimal path of the transmission line, efficiently collect the image data of the transmission line, ensure that each transmission line can be inspected, improve the integrity of the data, perform distortion correction on each line image in the line image set, obtain a correction image set, extract one correction image from the correction image set in turn, and perform the following operations on the extracted correction images: the present invention can correct the geometric distortion of the image through distortion correction, improve the clarity and accuracy of the image, and the corrected image is more convenient for subsequent analysis and processing. The insulation damage point of the correction image is identified to obtain a damage area set, and the voltage strength set of the damage area set is obtained, wherein the damage area set includes one or more damage areas. The present invention can highlight the insulation damage point by identifying the insulation damage point. Abnormal areas such as skin damage are easy to identify. If it is confirmed that the voltage intensity is concentrated with a voltage intensity greater than a preset voltage intensity threshold, a pre-constructed alarm signal is sent to a pre-constructed maintenance unit. After the maintenance unit receives the alarm signal, the damaged area is repaired by the maintenance unit to obtain a maintenance area, and the maintenance area is summarized to obtain a maintenance area group. When a high-voltage area is detected, the present invention immediately issues an alarm to remind relevant personnel to take emergency measures, summarizes the maintenance area group corresponding to the orderly inspection area, and obtains a maintenance area group set. The present invention centrally processes maintenance tasks, optimizes the allocation and utilization of maintenance resources, constructs a maintenance database based on the maintenance area group set, and completes distributed AI intelligent remote inspection based on edge computing based on the maintenance database.The maintenance database can record historical maintenance data, provide reference for future inspections and maintenance, and realize data-driven decision-making. Therefore, the present invention can improve the efficiency and reliability of remote inspections.

[0067] like Figure 2 As shown, it is a functional module diagram of a distributed AI intelligent remote inspection system based on edge computing provided by one embodiment of the present invention.

[0068] The distributed AI intelligent remote inspection system 100 based on edge computing of the present invention can be installed in an electronic device. According to the functions implemented, the distributed AI intelligent remote inspection system 100 based on edge computing can include an inspection area management module 101, an inspection path planning module 102, a line image processing module 103 and a line maintenance management module 104. The module of the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device; The inspection area management module 101 is used to obtain an inspection area set, and sort the inspection area set by regions using a preset inspection order to obtain an ordered inspection area set, wherein an ordered inspection area includes a substation, and the substation includes: a plurality of transmission lines; The inspection path planning module 102 is used to extract an orderly inspection area from the orderly inspection area set in turn, and perform the following operations on the extracted orderly inspection areas: obtain the electromagnetic interference intensity of the orderly inspection area, set the optimal inspection path according to the electromagnetic interference intensity, collect the ambient brightness, confirm the state of the lighting module according to the ambient brightness, and obtain the state of the lighting module, wherein the lighting module includes: a minus-one counter, a zero detector and a cycle counter, receive an intelligent remote inspection instruction, and perform line inspection on multiple transmission lines in the orderly inspection area according to the intelligent remote inspection instruction, the state of the lighting module and the optimal inspection path to obtain a line image set, wherein one line image includes one or more transmission lines; The line image processing module 103 is used to perform distortion correction on each line image in the line image set to obtain a correction image set, extract a correction image from the correction image set in turn, and perform the following operations on the extracted correction images: identify insulation damage points on the correction image to obtain a damage area set, and obtain a voltage intensity set of the damage area set, wherein the damage area set includes one or more damage areas; The line maintenance management module 104 is used to send a pre-built alarm signal to a pre-built maintenance unit if it is confirmed that there is a voltage intensity greater than a preset voltage intensity threshold in the voltage intensity concentration. When the maintenance unit receives the alarm signal, it uses the maintenance unit to repair the damaged area to obtain a maintenance area, and summarizes the maintenance areas to obtain a maintenance area group, summarizes the maintenance area groups corresponding to the ordered inspection areas to obtain a maintenance area group set, builds a maintenance database based on the maintenance area group set, and completes distributed AI intelligent remote inspection based on edge computing based on the maintenance database.

[0069] In detail, each module in the distributed AI intelligent remote inspection system 100 based on edge computing in the embodiment of the present invention is used in the same manner as above. Figure 1 The distributed AI intelligent remote inspection method based on edge computing has the same technical means and can produce the same technical effects, so I will not go into details here.

[0070] like Figure 3 As shown, it is a structural diagram of an electronic device for implementing a distributed AI intelligent remote inspection method based on edge computing provided by one embodiment of the present invention.

[0071] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a distributed AI intelligent remote inspection method program based on edge computing.

[0072] Among them, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 can also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the distributed AI intelligent remote inspection method program based on edge computing, but also can be used to temporarily store data that has been output or is to be output.

[0073] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect the various components of the entire electronic device, and executes or executes the programs or modules stored in the memory 11 (such as the distributed AI intelligent remote inspection method program based on edge computing, etc.), and calls the data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0074] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0075] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0076] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0077] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0078] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0079] The distributed AI intelligent remote inspection method program based on edge computing stored in the memory 11 in the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve: Obtain an inspection area set, and use a preset inspection order to sort the inspection area set to obtain an ordered inspection area set, wherein an ordered inspection area includes a substation, and the substation includes: a plurality of transmission lines; An ordered inspection area is extracted from the ordered inspection area set in sequence, and the following operations are performed on the extracted ordered inspection areas: Obtain the electromagnetic interference intensity of the orderly inspection area and set the optimal inspection path according to the electromagnetic interference intensity; Collecting the ambient brightness, confirming the state of the lighting module according to the ambient brightness, and obtaining the state of the lighting module, wherein the lighting module includes: a minus-one counter, a zero detector, and a cycle counter; receiving an intelligent remote inspection instruction, and performing line inspection on a plurality of power transmission lines in the orderly inspection area according to the intelligent remote inspection instruction, the state of the lighting module and the optimal inspection path, to obtain a line image set, wherein one line image includes one or more power transmission lines; Each line image in the line image set is subjected to distortion correction to obtain a correction image set, and one correction image is extracted from the correction image set in turn, and the following operations are performed on each of the extracted correction images: Identify insulation damage points on the corrected image to obtain a damage area set, and obtain a voltage intensity set of the damage area set, wherein the damage area set includes one or more damage areas; If it is confirmed that there is a voltage intensity greater than a preset voltage intensity threshold in the voltage intensity concentration, a pre-constructed alarm signal is sent to a pre-constructed maintenance unit. After the maintenance unit receives the alarm signal, the maintenance unit is used to repair the damaged area to obtain a maintenance area, and the maintenance areas are summarized to obtain a maintenance area group. Summarizing the maintenance area groups corresponding to the orderly inspection areas to obtain a maintenance area group set; Build a maintenance database based on maintenance area groups, and complete distributed AI intelligent remote inspection based on edge computing based on the maintenance database.

[0080] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0081] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0082] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement: Obtain an inspection area set, and use a preset inspection order to sort the inspection area set to obtain an ordered inspection area set, wherein an ordered inspection area includes a substation, and the substation includes: a plurality of transmission lines; An ordered inspection area is extracted from the ordered inspection area set in sequence, and the following operations are performed on the extracted ordered inspection areas: Obtain the electromagnetic interference intensity of the orderly inspection area and set the optimal inspection path according to the electromagnetic interference intensity; Collecting the ambient brightness, confirming the state of the lighting module according to the ambient brightness, and obtaining the state of the lighting module, wherein the lighting module includes: a minus-one counter, a zero detector, and a cycle counter; receiving an intelligent remote inspection instruction, and performing line inspection on a plurality of power transmission lines in the orderly inspection area according to the intelligent remote inspection instruction, the state of the lighting module and the optimal inspection path, to obtain a line image set, wherein one line image includes one or more power transmission lines; Each line image in the line image set is subjected to distortion correction to obtain a correction image set, and one correction image is extracted from the correction image set in turn, and the following operations are performed on each of the extracted correction images: Identify insulation damage points on the corrected image to obtain a damage area set, and obtain a voltage intensity set of the damage area set, wherein the damage area set includes one or more damage areas; If it is confirmed that there is a voltage intensity greater than a preset voltage intensity threshold in the voltage intensity concentration, a pre-constructed alarm signal is sent to a pre-constructed maintenance unit. After the maintenance unit receives the alarm signal, the maintenance unit is used to repair the damaged area to obtain a maintenance area, and the maintenance areas are summarized to obtain a maintenance area group. Summarizing the maintenance area groups corresponding to the orderly inspection areas to obtain a maintenance area group set; Build a maintenance database based on maintenance area groups, and complete distributed AI intelligent remote inspection based on edge computing based on the maintenance database.

[0083] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, and actual implementation may have other division methods.

[0084] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0085] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0086] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A distributed AI intelligent remote inspection method based on edge computing, characterized in that: The method comprises: Obtain an inspection area set, and use a preset inspection order to sort the inspection area set to obtain an ordered inspection area set, wherein an ordered inspection area includes a substation, and the substation includes: a plurality of transmission lines; An ordered inspection area is extracted from the ordered inspection area set in sequence, and the following operations are performed on the extracted ordered inspection areas: Obtain the electromagnetic interference intensity of the orderly inspection area and set the optimal inspection path according to the electromagnetic interference intensity; Collecting the ambient brightness, confirming the state of the lighting module according to the ambient brightness, and obtaining the state of the lighting module, wherein the lighting module includes: a minus-one counter, a zero detector, and a cycle counter; receiving an intelligent remote inspection instruction, and performing line inspection on a plurality of power transmission lines in the orderly inspection area according to the intelligent remote inspection instruction, the state of the lighting module and the optimal inspection path, to obtain a line image set, wherein one line image includes one or more power transmission lines; Each line image in the line image set is subjected to distortion correction to obtain a correction image set, and one correction image is extracted from the correction image set in turn, and the following operations are performed on each of the extracted correction images: Identify insulation damage points on the corrected image to obtain a damage area set, and obtain a voltage intensity set of the damage area set, wherein the damage area set includes one or more damage areas; If it is confirmed that there is a voltage intensity greater than a preset voltage intensity threshold in the voltage intensity concentration, a pre-constructed alarm signal is sent to a pre-constructed maintenance unit. After the maintenance unit receives the alarm signal, the maintenance unit is used to repair the damaged area to obtain a maintenance area, and the maintenance areas are summarized to obtain a maintenance area group. Summarizing the maintenance area groups corresponding to the orderly inspection areas to obtain a maintenance area group set; Build a maintenance database based on maintenance area groups, and complete distributed AI intelligent remote inspection based on edge computing based on the maintenance database.

2. The distributed AI intelligent remote inspection method based on edge computing according to claim 1 is characterized in that: The obtaining of electromagnetic interference intensity in the orderly inspection area includes: Obtain regional environmental parameters and regional current intensity of the orderly inspection area, and obtain regional air density values ​​based on the regional environmental parameters, wherein the regional environmental parameters include: regional temperature and regional air pressure; Determine whether the regional air density value is within the preset theoretical density range; If it is confirmed that the regional air density value is within the theoretical density interval, the regional air density value is corrected using the regional current intensity to obtain a corrected density air value; The modified density air value is used to calculate the electromagnetic interference intensity of multiple power transmission lines in the orderly inspection area, wherein the calculation formula of the electromagnetic interference intensity is as follows: ， ， in, Indicates the corrected density air value, Indicates the preset standard temperature. represents the regional temperature, represents the regional air pressure, Indicates the preset standard air pressure. represents the regional current intensity, Indicates the electromagnetic interference intensity. Indicates the preset insulation roughness coefficient. Indicates the preset transmission line radius.

3. The distributed AI intelligent remote inspection method based on edge computing as claimed in claim 2, characterized in that: The step of setting the optimal inspection path according to the electromagnetic interference intensity includes: The electromagnetic intensity attenuation value is obtained according to the electromagnetic interference intensity, and the inspection field length and inspection field width of the pre-built UAV are determined based on the electromagnetic intensity attenuation value, wherein the UAV includes: a camera, and the inspection field length and inspection field width are expressed as: ， ， ， in, Indicates the length of the inspection field of view. Indicates the inspection field width. represents the proportional adjustment coefficient, Indicates the length of the camera, Indicates the width of the camera, Indicates the preset flight altitude. Indicates the relative height difference of the camera. Represents the electromagnetic intensity attenuation value, Indicates the electromagnetic interference demarcation point, Indicates the line measurement distance; The optimal inspection path in the ordered inspection area is calculated using the pre-built optimal path formula, inspection field length, and inspection field width.

4. The distributed AI intelligent remote inspection method based on edge computing as claimed in claim 3 is characterized in that: The optimal path formula is as follows: ， in, represents the optimal inspection path, Indicates the total inspection length of the drone. represents the line spacing of the transmission line, Represents the number of transmission lines.

5. The distributed AI intelligent remote inspection method based on edge computing as claimed in claim 4 is characterized in that: The step of determining the state of the lighting module according to the ambient brightness to obtain the state of the lighting module includes: Get the ambient brightness value according to the ambient brightness; If the ambient brightness value is within the preset standard inspection brightness range, a start signal is obtained, the lighting module is turned on based on the start signal, and after confirming that the turned-on lighting module is the preset turn-on lighting module, the all-zero detector is confirmed as the initial state indicator; If the ambient brightness value is not within the preset standard inspection brightness range, a shutdown signal is obtained, the lighting module is turned off based on the shutdown signal, and after confirming that the turned-off lighting module is the preset turned-off lighting module, the all-zero detector is confirmed as the initial state indicator; The initial value of the decrement counter is set based on the initial state indicator and the cycle counter to obtain the initial value N of the decrement counter, and a decrement operation is performed on the initial value N of the decrement counter using a pre-constructed frequency-divided clock signal to obtain a decrement output value; If the decrement output value is not zero, the decrement output value is used as the initial value N of the decrement counter, and the process returns to the step of performing a decrement operation on the initial value N of the decrement counter using the pre-built divided clock signal until the decrement output value is zero, then the initial state indicator is confirmed to be a target all-zero detector, and the state of the lighting module is confirmed based on the target all-zero detector.

6. The distributed AI intelligent remote inspection method based on edge computing as claimed in claim 5, characterized in that: The step of performing distortion correction on each line image in the line image set to obtain a corrected image set includes: Extract one line image from the line image set in turn, and perform the following operations on each of the extracted line images: Construct a plane rectangular coordinate system according to the line image, obtain the coordinates of each pixel point in the line image according to the plane rectangular coordinate system, and obtain a pixel point coordinate set; Extract one pixel point coordinate from the pixel point coordinate set one by one, and perform the following operations on the extracted pixel point coordinates: The pixel coordinates are distorted and corrected to obtain the corrected pixel coordinates, where the corrected pixel coordinates are expressed as: ， in, Indicates the coordinates of the corrected pixel points. Represents the pixel coordinates, ( ) represents the coordinate deviation value, represents the radial distortion parameter, represents the tangential distortion parameter, Indicates the horizontal coordinate of the corrected pixel coordinates, Indicates the ordinate of the corrected pixel coordinates, Represents the horizontal coordinate of the pixel point, Indicates the vertical coordinate of the pixel; Summarizing the coordinates of the corrected pixel points to obtain a corrected pixel point coordinate set, and acquiring a corrected image based on the corrected pixel point coordinate set; The corrected images are aggregated to obtain a corrected image set.

7. The distributed AI intelligent remote inspection method based on edge computing according to claim 6 is characterized in that: The step of identifying insulation damage points on the corrected image to obtain a damaged area set includes: Identifying a target power transmission line from the corrected image, wherein the corrected image includes a plurality of target power transmission lines; Perform the following operations on the target transmission lines: Dividing the target transmission line by using a preset division range to obtain multiple line segments; Extract one route segment from multiple route segments one by one, and perform the following operations on each of the extracted route segments: Obtain a standard grayscale interval, filter the line segment to obtain a filtered line segment, perform grayscale conversion on the filtered line segment to obtain a grayscale line segment, wherein the grayscale line segment includes: a plurality of grayscale pixels, and the grayscale pixel includes: a grayscale pixel value, wherein the grayscale pixel value is the grayscale value of the grayscale pixel; The following operations are performed on each of the multiple grayscale pixels: Determine whether the grayscale pixel value corresponding to the grayscale pixel is within the standard grayscale interval; If the grayscale pixel value corresponding to the grayscale pixel is not in the standard grayscale interval, the grayscale pixel is recorded as an abnormal pixel; Summarize the abnormal pixels to obtain multiple abnormal pixels, confirm the number of abnormal pixels among the multiple abnormal pixels, and obtain the number of abnormal pixels; Compare the number of abnormal pixels with the preset standard number of pixels; If the number of abnormal pixels is greater than the preset number of standard pixels, a unit damaged area composed of multiple abnormal pixels is obtained; If the number of abnormal pixels is not greater than the preset number of standard pixels, returning to the step of extracting one line segment from the multiple line segments in sequence until all the multiple line segments are extracted; The unit damaged areas are summarized to obtain a unit damaged area group corresponding to the target transmission line, and the unit damaged area groups are summarized to obtain a damaged area set corresponding to the corrected image.

8. The distributed AI intelligent remote inspection method based on edge computing as claimed in claim 7, characterized in that: The step of obtaining the voltage intensity set of the damaged area set includes: The damaged areas are extracted from the damaged area set in sequence, and the following operations are performed on the extracted damaged areas: De-noise the damaged area to obtain a denoised area image; Extract edge points from the denoised area image to obtain an edge point set, calculate the leakage voltage component of each edge point in the edge point set, and sum the leakage voltage components of each edge point in the edge point set to obtain a total leakage voltage component; The voltage intensity of the damaged area is calculated based on the total leakage voltage component, where the voltage intensity is calculated as follows: ， in, Indicates the voltage intensity, represents pi, represents the capacitance parameter of the transmission line, Indicates the edge point concentration The leakage voltage component of each edge point is Indicates the number of edge points in the edge point set; The voltage intensities are summed up to obtain a voltage intensity set.

9. The distributed AI intelligent remote inspection method based on edge computing as claimed in claim 8, characterized in that: Denoising the damaged area to obtain a denoised area image includes: The pre-built Gaussian kernel formula is used to perform convolution operation on each damaged image pixel in the damaged area image to obtain a denoised pixel point set, where the Gaussian kernel formula is as follows: ， in, represents the denoised pixel, represents the standard deviation of the Gaussian function, represents the exponential function with base e as base, Represents the horizontal coordinate of the denoised pixel, Represents the ordinate of the denoised pixel point, Indicates the center position of the Gaussian kernel; A denoised area image is confirmed according to the denoised pixel point set.

10. A distributed AI intelligent remote inspection system based on edge computing, characterized in that: The system comprises: The inspection area management module is used to obtain an inspection area set, and use a preset inspection order to sort the inspection area set to obtain an ordered inspection area set, wherein an ordered inspection area includes a substation, and the substation includes: multiple transmission lines; The inspection path planning module is used to extract an orderly inspection area from the orderly inspection area set in turn, and perform the following operations on the extracted orderly inspection areas: obtain the electromagnetic interference intensity of the orderly inspection area, set the optimal inspection path according to the electromagnetic interference intensity, collect the ambient brightness, confirm the state of the lighting module according to the ambient brightness, and obtain the state of the lighting module, wherein the lighting module includes: a minus-one counter, a zero detector and a cycle counter, receive intelligent remote inspection instructions, and perform line inspection on multiple power transmission lines in the orderly inspection area according to the intelligent remote inspection instructions, the state of the lighting module and the optimal inspection path to obtain a line image set, wherein one line image includes one or more power transmission lines; The line image processing module is used to perform distortion correction on each line image in the line image set to obtain a correction image set, extract a correction image from the correction image set in turn, and perform the following operations on the extracted correction images: identify insulation damage points on the correction image to obtain a damage area set, and obtain a voltage intensity set of the damage area set, wherein the damage area set includes one or more damage areas; The line maintenance management module is used to send a pre-built alarm signal to a pre-built maintenance unit if it is confirmed that there is a voltage intensity greater than a preset voltage intensity threshold in the voltage intensity concentration. When the maintenance unit receives the alarm signal, it uses the maintenance unit to repair the damaged area to obtain the maintenance area, and summarizes the maintenance area to obtain a maintenance area group, summarizes the maintenance area groups corresponding to the ordered inspection areas to obtain a maintenance area group set, builds a maintenance database based on the maintenance area group set, and completes distributed AI intelligent remote inspection based on edge computing based on the maintenance database.

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